Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma

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ID: 118259
2017
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Abstract
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feat …
Reference Key
b2017cancerradiomic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang B;He X;Ouyang F;Gu D;Dong Y;Zhang L;Mo X;Huang W;Tian J;Zhang S;;
Journal cancer letters
Year 2017
DOI
DOI not found
URL
https://www.ncbi.nlm.nih.gov/pubmed/28610955
Keywords
National Center for Biotechnology Information NCBI NLM MEDLINE diagnosis humans pubmed abstract nih national institutes of health national library of medicine reproducibility of results Comparative Study risk factors linear models computer-assisted / methods* nasopharyngeal carcinoma nasopharyngeal neoplasms / pathology predictive value of tests risk assessment roc curve area under curve image interpretation magnetic resonance imaging / methods* decision support techniques* treatment failure pmid:28610955 doi:10.1016/j.canlet.2017.06.004 bin zhang xin he shuixing zhang carcinoma / diagnostic imaging* carcinoma / pathology carcinoma / therapy high-throughput screening assays nasopharyngeal neoplasms / diagnostic imaging* nasopharyngeal neoplasms / therapy support vector machine*

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